https://github.com/3dlg-hcvc/paris
공식 코드PARIS: Part-level Reconstruction and Motion Analysis for Articulated Objects
Papers with Code 아카이브
Motion Estimation
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Motion Estimation is used to determine the block-wise or pixel-wise motion vectors between two frames.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ComputationalPhysiology/automatic-motion-estimation-hiPSC-CM
공식 코드https://github.com/Forrest-110/FluidMotionNet
공식 코드Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss
https://github.com/HZXu-526/Human-Avatar
공식 코드Efficient Neural Implicit Representation for 3D Human Reconstruction
https://github.com/HalleyJiang/DiPE
공식 코드https://github.com/HanLingsgjk/CSCV
공식 코드ScaleFlow++: Robust and Accurate Estimation of 3D Motion from Video
https://github.com/HendrikMuenster/superResolution
공식 코드https://github.com/JunHeum/BMBC
공식 코드BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation
https://github.com/KUIS-AI-Tekalp-Research-Group/video-compression
공식 코드End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
https://github.com/KUIS-AI-Tekalp-Research-Group/video-compression/tree/master/Flex-Rate-Hier-Bidir-Video-Compression
공식 코드https://github.com/MBDS/multibody-state-estimation
공식 코드A general framework for modeling and dynamic simulation of multibody systems using factor graphs
https://github.com/MKashifAli/Motion_Blind_Video_Stabilization
공식 코드https://github.com/Meixu-Chen/MOVI-Codec
공식 코드https://github.com/NGCLAB/multi-spectral-dataset
공식 코드A Multi-spectral Dataset for Evaluating Motion Estimation Systems
https://github.com/NJUVISION/DHVC
공식 코드High-Efficiency Neural Video Compression via Hierarchical Predictive Learning
https://github.com/NagabhushanSN95/DeCOMPnet
공식 코드https://github.com/Netflix/vmaf
공식 코드SpatioTemporal Feature Integration and Model Fusion for Full Reference Video Quality Assessment
https://github.com/RViMLab/oflibnumpy
공식 코드Oflib: Facilitating Operations with and on Optical Flow Fields in Python
https://github.com/RViMLab/oflibpytorch
공식 코드Oflib: Facilitating Operations with and on Optical Flow Fields in Python
https://github.com/RuijieZhu94/MotionGS
공식 코드MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian Splatting
https://github.com/SeokjuLee/Insta-DM
공식 코드Instance-wise Depth and Motion Learning from Monocular Videos
https://github.com/TRI-ML/KP3D
공식 코드Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
https://github.com/TRI-ML/packnet-sfm
공식 코드Neural Ray Surfaces for Self-Supervised Learning of Depth and Ego-motion
https://github.com/Totoro97/Vid2Curve
공식 코드Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
https://github.com/Uehwan/rmd
공식 코드https://github.com/Visillect/xvcm-dataset
공식 코드https://github.com/WU-CVGL/USB-NeRF
공식 코드USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields
https://github.com/Xianhang/EDSC-pytorch
공식 코드Multiple Video Frame Interpolation via Enhanced Deformable Separable Convolution
https://github.com/acnext/optical-flow-for-spiking-camera
공식 코드https://github.com/adnortje/deepvideo
공식 코드Deep motion estimation for parallel inter-frame prediction in video compression
https://github.com/aharley/pips
공식 코드Particle Video Revisited: Tracking Through Occlusions Using Point Trajectories
https://github.com/ahmedhumais/e-stmflow
공식 코드Spatio-Temporal State Space Model For Efficient Event-Based Optical Flow
https://github.com/aipixel/gaussianavatar
공식 코드https://github.com/alexandervakhitov/sego-paper-code
공식 코드https://github.com/alexsunnik/refining-motion-code
공식 코드https://github.com/anuragranj/cc
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.